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Some scholars classify cherry-picking as a fallacy of selective attention, the most common example of which is the confirmation bias. [3] Cherry picking can refer to the selection of data or data sets so a study or survey will give desired, predictable results which may be misleading or even completely contrary to reality. [4]
In particular, rather than spreading out evenly, it is not uncommon for random data points to form clusters, giving the (false) impression of "hot spots" created by some underlying cause. The Texas sharpshooter fallacy often arises when a person has a large amount of data at their disposal but only focuses on a small subset of that data.
Cherry picking, which actually is not selection bias, but confirmation bias, when specific subsets of data are chosen to support a conclusion (e.g. citing examples of plane crashes as evidence of airline flight being unsafe, while ignoring the far more common example of flights that complete safely.
Cherry picking (suppressed evidence, incomplete evidence, argument by half-truth, fallacy of exclusion, card stacking, slanting) – using individual cases or data that confirm a particular position, while ignoring related cases or data that may contradict that position.
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Data dredging is an example of disregarding the multiple comparisons problem. One form is when subgroups are compared without alerting the reader to the total number of subgroup comparisons examined. One form is when subgroups are compared without alerting the reader to the total number of subgroup comparisons examined.
That comment has led many health experts, including those who spoke to The Hill, to believe that Kennedy would likely cherry-pick data so that he could direct the director of the CDC or the ...
X accused the center of breaching its 2019 user contract by scraping and cherry-picking data to create false and misleading reports that Musk turned X into a haven for hate speech, extremism and ...